MICDE awards seven Catalyst Grants

The Michigan Institute for Computational Discovery and Engineering has awarded its second round of Catalyst Grants, providing between $80,000 and $90,000 each to seven innovative projects in computational science. The proposals were judged on novelty, likelihood of success at catalyzing larger programs and potential to leverage ARC’s computing resources.

The funded projects are:

Title: Exploring Quantum Embedding Methods for Quantum ComputingResearchers: Emanuel Gull, Physics; Dominika Zgid, Chemistry.Description: The research team will design quantum embedding algorithms that can be early adopters of quantum computers on development of advanced materials for possible applications in modern batteries, next-generation oxide electronics, or high-temperature superconducting power cables.

Title: Teaching autonomous soft machines to swimResearchers: Silas Alben, Mathematics; Robert Deegan, Physics, Alex Gorodetsky, Aerospace EngineeringDescription: Self-oscillating gels are polymeric materials that change shape, driven by chemical reactions occurring entirely within the gel. The research team will develop a computational and machine learning program to discover how to configure self-oscillating gels so that they undergo deformations that result in swimming. The long term goal is to develop a general framework for controlling autonomous soft machines.

Title: Advancing the Computational Frontiers of Solution-Adaptive, Scale-Aware Climate ModelsResearchers: Christiane Jablonowski, Climate and Space Sciences and Engineering; Hans Johansen, Lawrence Berkeley National Lab.Description: Researchers will further develop a 3-D mesh adaptation model for climate modeling, allowing computational resources to be focused on phenomena of interest such as tropical cyclones or other extreme weather events. The project will also introduce data-driven machine learning paradigms into modeling of clouds and precipitation.

Title: Deciphering the meaning of human brain rhythms using novel algorithms and massive, rare datasetsResearchers: Omar Ahmed, Psychology, Neuroscience and Biomedical EngineeringDescription: The team will develop a set of algorithms for use on high performance computers to analyze de-identified brain data from patients in order to better understand what electrical oscillations tell us about rapidly changing behavioral and pathological brain states.

Title: Embedded Machine Learning Systems To Sense and Understand Pollinator BehaviorResearchers: Robert Dick, Electrical Engineering and Computer Science; Fernanda Valdovinos Ecology and Evolutionary Biology, Center for Complex Systems; Paul Glaum, Ecology and Evolutionary Biology.Description: To understand the mechanisms driving the population dynamics of pollinators, the research team will develop technologies for deeply embedded hardware/software learning systems capable of remote, long term, autonomous operation; and will analyze the resulting new data to better understand pollinator activity.

Title: Deep Learning for Phylogenetic InferenceResearchers: Jianzhi Zhang, Ecology and Evolutionary Biology; Yuanfang Guan, Computational Medicine and Bioinformatics.Description: The research team will use deep neural networks to infer molecular phylogenies and extract phylogenetically useful patterns from amino acid or nucleotide sequences, which will help understand evolutionary mechanisms and build evolutionary models for a variety of analyses.

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